class_docstr: Manages a PyTorch model for training.
class_docstr_tok: ["Manages","a","PyTorch","model","for","training","."]
docstr: Evaluates the model on the validation data set.
docstr_tok: ["Evaluates","the","model","on","the","validation","data","set","."]
code: def validate(
      self,
      data_creator,
      batch_size=32,
      num_steps=None,
      profile=False,
      wrap_dataloader=None,
      callbacks=None,
  ):
      """Evaluates the model on the validation data set."""
      self.load_state_dict(self.state_dict.value)
      validation_stats = super().validate(
          data_creator,
          batch_size,
          num_steps,
          profile,
          wrap_dataloader,
          callbacks,
      )
      if self.log_to_driver:
          LogMonitor.stop_log_monitor(
              self.log_path, self.logger_thread, self.thread_stop
          )
      return [validation_stats]
  
file_path: BigDL/python/orca/src/bigdl/orca/learn/pytorch/pytorch_pyspark_worker.py
imports: from bigdl.orca.learn.pytorch.torch_runner import TorchRunner
  import torch.distributed
  import logging
  import os
  import tempfile
  import copy
  from pyspark import BarrierTaskContext, TaskContext
  from bigdl.orca.learn.utils import save_pkl, duplicate_stdout_stderr_to_file, get_rank, get_partition_id
  from bigdl.orca.learn.log_monitor import LogMonitor
label_desc: Use image node_id 5 for calling the PytorchPysparkWorker obj's underlying member method code with example usage: obj.validate(data_creator, batch_size, num_steps, profile, wrap_dataloader, callbacks) and returns: list